
Q6. DESCRIBE THE STRUCTURE OF ARTIFICIAL NEURAL NETWORKS? ............................................................................. 57
Q7. HOW ARE WEIGHTS INITIALIZED IN A NETWORK? ............................................................................................... 57
Q8. WHAT IS THE COST FUNCTION? ....................................................................................................................... 58
Q9. WHAT ARE HYPERPARAMETERS? ..................................................................................................................... 58
Q10. WHAT WILL HAPPEN IF THE LEARNING RATE IS SET INACCURATELY (TOO LOW OR TOO HIGH)? ................................... 58
Q11. WHAT IS THE DIFFERENCE BETWEEN EPOCH, BATCH, AND ITERATION IN DEEP LEARNING? ......................................... 58
Q12. WHAT ARE THE DIFFERENT LAYERS ON CNN? .................................................................................................... 58
Convolution Operation ...................................................................................................................................... 60
Pooling Operation ............................................................................................................................................. 62
Classification ..................................................................................................................................................... 63
Training ............................................................................................................................................................. 64
Testing ............................................................................................................................................................... 65
Q13. WHAT IS POOLING ON CNN, AND HOW DOES IT WORK? .................................................................................... 65
Q14. WHAT ARE RECURRENT NEURAL NETWORKS (RNNS)? ........................................................................................ 65
Parameter Sharing ............................................................................................................................................ 67
Deep RNNs ......................................................................................................................................................... 68
Bidirectional RNNs ............................................................................................................................................. 68
Recursive Neural Network ................................................................................................................................. 69
Encoder Decoder Sequence to Sequence RNNs ................................................................................................. 70
LSTMs ................................................................................................................................................................ 70
Q15. HOW DOES AN LSTM NETWORK WORK? ......................................................................................................... 70
Recurrent Neural Networks ............................................................................................................................... 71
The Problem of Long-Term Dependencies ......................................................................................................... 72
LSTM Networks .................................................................................................................................................. 73
The Core Idea Behind LSTMs ............................................................................................................................. 74
Q16. WHAT IS A MULTI-LAYER PERCEPTRON (MLP)? ................................................................................................. 75
Q17. EXPLAIN GRADIENT DESCENT. ......................................................................................................................... 76
Q18. WHAT IS EXPLODING GRADIENTS? .................................................................................................................... 77
Solutions ............................................................................................................................................................ 78
Q19. WHAT IS VANISHING GRADIENTS? .................................................................................................................... 78
Solutions ............................................................................................................................................................ 79
Q20. WHAT IS BACK PROPAGATION AND EXPLAIN IT WORKS. ....................................................................................... 79
Q21. WHAT ARE THE VARIANTS OF BACK PROPAGATION? ............................................................................................ 79
Q22. WHAT ARE THE DIFFERENT DEEP LEARNING FRAMEWORKS? .................................................................................. 81
Q23. WHAT IS THE ROLE OF THE ACTIVATION FUNCTION? ............................................................................................ 81
Q24. NAME A FEW MACHINE LEARNING LIBRARIES FOR VARIOUS PURPOSES. .................................................................... 81
Q25. WHAT IS AN AUTO-ENCODER? ........................................................................................................................ 81
Q26. WHAT IS A BOLTZMANN MACHINE? ................................................................................................................. 82
Q27. WHAT IS DROPOUT AND BATCH NORMALIZATION? ............................................................................................. 83
Q28. WHY IS TENSORFLOW THE MOST PREFERRED LIBRARY IN DEEP LEARNING? ............................................................. 83
Q29. WHAT DO YOU MEAN BY TENSOR IN TENSORFLOW? .......................................................................................... 83
Q30. WHAT IS THE COMPUTATIONAL GRAPH? ........................................................................................................... 83
Q31. HOW IS LOGISTIC REGRESSION DONE? ............................................................................................................... 83
MISCELLANEOUS ................................................................................................................................................ 84
Q1. EXPLAIN THE STEPS IN MAKING A DECISION TREE. ................................................................................................. 84
Q2. HOW DO YOU BUILD A RANDOM FOREST MODEL? ................................................................................................ 84
Q3. DIFFERENTIATE BETWEEN UNIVARIATE, BIVARIATE, AND MULTIVARIATE ANALYSIS. ...................................................... 85
Univariate .......................................................................................................................................................... 85
Bivariate ............................................................................................................................................................ 85
Multivariate ....................................................................................................................................................... 85
Q4. WHAT ARE THE FEATURE SELECTION METHODS USED TO SELECT THE RIGHT VARIABLES? .............................................. 86
Filter Methods ................................................................................................................................................... 86